package com.heima.article.service.impl;

import com.alibaba.fastjson.JSON;
import com.heima.article.mapper.ApArticleMapper;
import com.heima.article.service.HotArticleService;
import com.heima.common.article.ArticleConstants;
import com.heima.common.article.HotArticleConstants;
import com.heima.common.exception.CustException;
import com.heima.feigns.AdminFeign;
import com.heima.model.admin.pojos.AdChannel;
import com.heima.model.article.pojos.ApArticle;
import com.heima.model.article.vos.HotArticleVo;
import com.heima.model.common.dtos.ResponseResult;
import com.heima.model.common.enums.AppHttpCodeEnum;
import com.heima.model.mess.app.AggBehaviorDTO;
import com.heima.utils.common.DateUtils;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.BeanUtils;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.redis.core.StringRedisTemplate;
import org.springframework.stereotype.Service;
import org.springframework.util.CollectionUtils;
import org.springframework.util.StringUtils;

import java.time.LocalDateTime;
import java.time.format.DateTimeFormatter;
import java.util.Calendar;
import java.util.Comparator;
import java.util.Date;
import java.util.List;
import java.util.stream.Collectors;

/**
 * @author mrchen
 * @date 2022/8/9 9:57
 */
@Service
@Slf4j
public class HotArticleServiceImpl implements HotArticleService {
    @Autowired
    ApArticleMapper apArticleMapper;
    @Autowired
    StringRedisTemplate redisTemplate;

    @Override
    public void computeHotArticle() {
        // 1. 查询近5天的文章数据
        List<ApArticle> list = getArticleList();
        if (CollectionUtils.isEmpty(list)) {
            log.info(" 太冷清了，近5天没人在我们的app上发表文章 ~~~");
            return;
        }
        // 2. 计算每篇文章的分值
        List<HotArticleVo> hotArticleVoList = getHotArticleVoList(list);

        // 3. 按照频道将每个频道最热门的30条文章缓存
        cacheRedisByTag(hotArticleVoList);
    }

    @Override
    public void updateApArticle(AggBehaviorDTO aggBehavior) {
        // 1. 根据聚合结果中的文章ID 查询文章数据
        ApArticle article = apArticleMapper.selectById(aggBehavior.getArticleId());
        // 2. 根据聚合结果中各行为的值 修改文章中行为总量的值
        article.setViews((int)(article.getViews() + aggBehavior.getView()));
        article.setLikes((int)(article.getLikes() + aggBehavior.getLike()));
        article.setComment((int)(article.getComment() + aggBehavior.getComment()));
        article.setCollection((int)(article.getCollection() + aggBehavior.getCollect()));
        // 3. 修改文章
        apArticleMapper.updateById(article);
        // 4. 重新计算文章热度得分
        Integer score = computeScore(article);
        // 5. 如果文章时今天发布的  整体热度 * 3
        String publishTime = DateUtils.dateToString(article.getPublishTime());
        String nowTime = DateUtils.dateToString(new Date());
        if(publishTime.equals(nowTime)){
            score = score * 3;
        }
        // 6. 查询对应的频道热点文章， 替换分值较低的热点文章
        updateArticleCache(article,score, ArticleConstants.HOT_ARTICLE_FIRST_PAGE + article.getChannelId());
        // 7. 查询对应的推荐频道热点文章， 替换分值较低的热点文章
        updateArticleCache(article,score, ArticleConstants.HOT_ARTICLE_FIRST_PAGE + ArticleConstants.DEFAULT_TAG);
    }

    /**
     * 更新缓存热点文章
     * @param article
     * @param score   最新文章热度得分
     * @param cacheKey   缓存key
     */
    private void updateArticleCache(ApArticle article, Integer score, String cacheKey) {
        // 1. 根据缓存key 查询对应的热点文章列表
        String hotArticleJson = redisTemplate.opsForValue().get(cacheKey);
        if (StringUtils.isEmpty(hotArticleJson)) {
            CustException.cust(AppHttpCodeEnum.SERVER_ERROR,"所属频道 热点文章列表为空");
        }
        List<HotArticleVo> hotArticleVoList = JSON.parseArray(hotArticleJson, HotArticleVo.class);
        // 2. 判断当前文章是否已经存在热点文章列表
        boolean isHas = false;
        for (HotArticleVo articleVo : hotArticleVoList) {
            // 2.1  如果已存在，修改热点分值
            if(articleVo.getId().equals(article.getId())){
                articleVo.setScore(score);
                isHas = true;
                break;
            }
        }
        // 2.2  如果不存在  将当前文章加入到热点文章列表
        if(!isHas){
            HotArticleVo articleVo = new HotArticleVo();
            BeanUtils.copyProperties(article,articleVo);
            articleVo.setScore(score);
            hotArticleVoList.add(articleVo);
        }
        // 3. 将热点文章按照热度 降序排序  截取前30条文章
        hotArticleVoList = hotArticleVoList.stream()
                        .sorted(Comparator.comparing(HotArticleVo::getScore).reversed())
                        .limit(30)
                        .collect(Collectors.toList());
        // 4. 保存redis缓存中
        redisTemplate.opsForValue().set(cacheKey,JSON.toJSONString(hotArticleVoList));
    }

    @Autowired
    AdminFeign adminFeign;

    /**
     * 将热点文章缓存到redis
     * @param hotArticleVoList
     */
    private void cacheRedisByTag(List<HotArticleVo> hotArticleVoList) {
        // 1. 使用feign查询频道列表
        ResponseResult<List<AdChannel>> result = adminFeign.selectChannels();
        if (!result.checkCode()) {
            CustException.cust(AppHttpCodeEnum.REMOTE_SERVER_ERROR,"远程查询频道列表失败");
        }
        List<AdChannel> channelList = result.getData();
        // 2. 遍历频道列表
        for (AdChannel channel : channelList) {
            // 找出每个频道对应的文章 进行缓存
            List<HotArticleVo> articleVoListByTag = hotArticleVoList.stream()
                    .filter(hotArticleVo -> hotArticleVo.getChannelId().equals(channel.getId()))
                    .collect(Collectors.toList());
            if (!CollectionUtils.isEmpty(articleVoListByTag)) {
                sortAndCache(articleVoListByTag,ArticleConstants.HOT_ARTICLE_FIRST_PAGE + channel.getId());
            }

        }
        // 3. 推荐频道
                // 直接根据所有文章数据   进行缓存
        sortAndCache(hotArticleVoList,ArticleConstants.HOT_ARTICLE_FIRST_PAGE + ArticleConstants.DEFAULT_TAG);
    }

    /**
     * 将文章缓存到redis
     * @param articleVoList
     * @param cacheKey
     */
    private void sortAndCache(List<HotArticleVo> articleVoList, String cacheKey) {
        // 将带有分值的文章列表     截取前30条文章
        List<HotArticleVo> hotArticleVoList = articleVoList.stream()
                // 按照文章热度 降序排序
                .sorted(Comparator.comparing(HotArticleVo::getScore).reversed())
                // 截取前30条文章
                .limit(30)
                .collect(Collectors.toList());
        // 将文章缓存到redis中
        redisTemplate.opsForValue().set(cacheKey, JSON.toJSONString(hotArticleVoList));
    }
    /**
     * 计算每篇文章热度值
     * @param list
     * @return
     */
    private List<HotArticleVo> getHotArticleVoList(List<ApArticle> list) {
        return list.stream()
                .map(article -> {
                    HotArticleVo articleVo = new HotArticleVo();
                    BeanUtils.copyProperties(article,articleVo);
                    Integer score = computeScore(article);
                    articleVo.setScore(score);
                    return articleVo;
                }).collect(Collectors.toList());
    }

    /**
     * 计算每篇文章的热度得分
     * @param article
     * @return
     */
    private Integer computeScore(ApArticle article) {
        Integer score = 0;
        // 阅读
        if(article.getViews()!=null){
            score += article.getViews() * ArticleConstants.HOT_ARTICLE_VIEW_WEIGHT;
        }
        // 点赞
        if(article.getLikes()!=null){
            score += article.getLikes() * ArticleConstants.HOT_ARTICLE_LIKE_WEIGHT;
        }
        // 评论
        if(article.getComment()!=null){
            score += article.getComment() * ArticleConstants.HOT_ARTICLE_COMMENT_WEIGHT;
        }
        // 收藏
        if(article.getCollection()!=null){
            score += article.getCollection() * ArticleConstants.HOT_ARTICLE_COLLECTION_WEIGHT;
        }
        return score;
    }

    /**
     * 查询近5天文章搞定
     * @return
     */
    private List<ApArticle> getArticleList() {
        // 1. 算计5天前的时间
        String dateParam = LocalDateTime.now()
                .minusDays(5)
                .format(DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss"));
        // 2. 调用articleMapper按照时间查询
        List<ApArticle> articleList = apArticleMapper.selectArticleByDate(dateParam);
        return articleList;
    }
}
